abhipsabasu
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- README.md +39 -0
- checkpoint-1000/model.safetensors +3 -0
- checkpoint-1000/optimizer.bin +3 -0
- checkpoint-1000/random_states_0.pkl +3 -0
- checkpoint-1000/scheduler.bin +3 -0
- checkpoint-1500/model.safetensors +3 -0
- checkpoint-1500/optimizer.bin +3 -0
- checkpoint-1500/random_states_0.pkl +3 -0
- checkpoint-1500/scheduler.bin +3 -0
- checkpoint-2000/model.safetensors +3 -0
- checkpoint-2000/optimizer.bin +3 -0
- checkpoint-2000/random_states_0.pkl +3 -0
- checkpoint-2000/scheduler.bin +3 -0
- checkpoint-2500/model.safetensors +3 -0
- checkpoint-2500/optimizer.bin +3 -0
- checkpoint-2500/random_states_0.pkl +3 -0
- checkpoint-2500/scheduler.bin +3 -0
- checkpoint-3000/model.safetensors +3 -0
- checkpoint-3000/optimizer.bin +3 -0
- checkpoint-3000/random_states_0.pkl +3 -0
- checkpoint-3000/scheduler.bin +3 -0
- checkpoint-3500/model.safetensors +3 -0
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- checkpoint-3500/random_states_0.pkl +3 -0
- checkpoint-3500/scheduler.bin +3 -0
- checkpoint-4000/model.safetensors +3 -0
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- checkpoint-4000/scheduler.bin +3 -0
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- checkpoint-5000/scheduler.bin +3 -0
- checkpoint-5500/model.safetensors +3 -0
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- checkpoint-5500/scheduler.bin +3 -0
- checkpoint-6000/model.safetensors +3 -0
- checkpoint-6000/optimizer.bin +3 -0
- checkpoint-6000/random_states_0.pkl +3 -0
- checkpoint-6000/scheduler.bin +3 -0
- feature_extractor/preprocessor_config.json +27 -0
README.md
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---
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base_model: runwayml/stable-diffusion-v1-5
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library_name: diffusers
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license: creativeml-openrail-m
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tags:
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- stable-diffusion
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- stable-diffusion-diffusers
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- text-to-image
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- diffusers
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- textual_inversion
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- diffusers-training
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inference: true
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---
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<!-- This model card has been generated automatically according to the information the training script had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Textual inversion text2image fine-tuning - abhipsabasu/textual_inversion_place3
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These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
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## Intended uses & limitations
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#### How to use
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```python
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# TODO: add an example code snippet for running this diffusion pipeline
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```
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#### Limitations and bias
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[TODO: provide examples of latent issues and potential remediations]
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## Training details
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[TODO: describe the data used to train the model]
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checkpoint-6000/scheduler.bin
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feature_extractor/preprocessor_config.json
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